Reducing cost of volatile risk estimates on financial instruments in a cloud environment

Abstract: Performing high accuracy risk calculations on financial instruments is a computationally heavy task. Since a day can contain a lot of different events that change the stock prices, it is important that these risk calculations are done in real time. Deploying a system to calculate real time risk in a cloud environment would result in high costs because of the heavy calculations that needs to be performed. This thesis is a practical continuation of a previous master thesis written by Sara Ekman [1]. Ekman proposed different strategies to reduce the amount of recalculations while still having real time precision on the risk values. In this thesis the strategies covering European options and Futures are implemented and tested over several days, with a goal of reducing the cost of a daily run by 5%, compared to using constant intervals. Some strategies upheld the goal in simulations but did however loose accuracy. The strategies that performed the best, under tested conditions, was the strategies that did not consider any scenarios when evaluating if recalculations had to be made.